s_wiener_filter.m

地震、测井方面matlab代码,解释的比较详细

function [filter,cc_max]=s_wiener_filter(from,to,varargin)
% Function computes filter that matches the first input data set to the 
% second input data set
% Written by: E. R.: 
% Last updated: August 4, 2004: handle case when first data set is longer than
%                               the second data set + the length of the filter;
%                               bug fix for filter start time
%
%             [filter,cc_max]=s_wiener_filter(from,to,varargin)
% INPUT
% from, to    seismic data sets to be matched. 
%             Depending on the options used the two data sets may 
%             need to have the same number of traces
%             If the lengthof "to" + the filter length is less than the
%             length of "to" a filter is computed for a number of shifts 
%             equal to "length ("to") - length ("from") - length('filter");
%             the best-matching filter is chosen
%             samples of "from"
% varargin    one or more cell arrays; the first element of each cell
%             array is a keyword,the other elements are parameters. 
%             Presently, keywords are:
%       'method'    defines the method/algorithm to be used to compute
%              the filter. Presently,the following algorithms are available:
%             'sridge'   simple ridge regression
%             
%       'option'    specifies which type of filter to compute. Presently,
%             the following options are available:
%             'tracewise'  compute filters (one filter for each input/output trace pair)
%                   which convert traces in the first input dataset into the
%                   corresponding traces of the second seismic structure input. This
%                   requires that the the two seismic structures have the same number of 
%                   traces (ntr1=ntr2). The number of filters output is equal to ntr1, the 
%                   number of traces in the input data sets.
%             'dataset'  Compute a single filter that converts every trace 
%                   in the first input dataset into the corresponding trace
%                   of the second seismic input dataset. This requires 
%                   that the the two seismic structures have the same number 
%                   of traces (ntr1=ntr2).
%             'all'    Compute ntr1*ntr2 individual filters which match each
%                   trace of the first input dataset to each trace 
%                   of the second input data set.
%       'flength'    filter length in ms; no default.
%       'window1'   window on the first input data set which should be used
%                   Default: the whole time range.
%       'window2'   window on the second input data set that should be matched             
%                   Default: the whole time range.
%       'null'      Null value to use for filter coefficients that do not exist
%                   Only important if there is more than one filter and if 
%                   filters are shifted relative to one another. Default: null=0.
%       'header"    As part of the filter calculation the crosscorrelation
%                   of the filtered first input data set, i.e. filter*from, 
%                   and second data set, to, is computed. 
%                   'header' defines the header name used to store 
%                   these crosscorrelations for option "tracewise".
%                   Default: {'header','param_cc'}
%       'wnoise'    fraction of "white noise" to add
%                   Default: {'wnoise',0.01}
% OUTPUT
% filter      filter in form of a seismic structure. Filter has either one trace (a single 
%             filter matching corresponding traces), or as many traces as there are traces
%             in from or to. In this case there is one filter for each pair of
%             corrsponding input traces. Finally there can be ntr1*ntr2 filters matching
%             trace of the first input data set to each trace of the second input data set.
%             In this case the first ntr2 filters match the first trace of the first input 
%             data set to the ntr2 traces of the second data set, the second ntr2 filters 
%             match the second trace of the first input data set to the second data set, etc.
% cc_max      Cross correlations between filter*from and to.


%      Check input data
if ~strcmp(from.type,'seismic')  |  ~strcmp(to.type,'seismic')
   error(' The first two input data sets must be seismic structures')
end

ntr1=size(from.traces,2);
ntr2=size(to.traces,2);

if from.step~= to.step
   error([' The two input data sets must have the same sample interval (', ...
          num2str(from.step),' differs from ',num2str(to.step),')'])
end

%    Set default values for input parameters
param.increment=from.step;
param.flength=[];
%param.method='sridge';
param.null=0;
param.option=[];
param.stepout=[];
param.scale='yes';
% param.type=1;
param.window1={from.first,from.last};
param.window2={to.first,to.last};
param.header='param_cc';
param.wnoise=0.01;

%       Replace defaults by actual input arguments
param=assign_input(param,varargin);

if isempty(param.option)
   error('Parameter "option" must be specified.')
end

if isempty(param.flength)
   error(' Parameter "flength" has no default; must be specified')
else
   nf=round(param.flength/from.step)+1;
%   nf2=round((nf+1)/2);
end


if strcmp(param.scale,'yes')
   scaling=logical(1);
else
   scaling=logical(0);
end


ta1=max([param.window1{1},from.first]);
te1=min([param.window1{2},from.last]);
ta2=max([param.window2{1},to.first]);
te2=min([param.window2{2},to.last]);
ia1=round((ta1-from.first)/from.step)+1;
ie1=round((te1-from.first)/from.step)+1;
ia2=round((ta2-to.first)/to.step)+1;
ie2=round((te2-to.first)/to.step)+1;
n1=ie1-ia1+1;
n2=ie2-ia2+1;

if isempty(param.stepout)
   param.stepout=param.flength/10;
end 
incr=max(1,round(param.stepout/from.step));

filter.type='seismic';
filter.tag='wavelet';
filter.name='filter';


% 	Compute Wiener filter.

switch param.option

               case 'tracewise'
if ntr1 ~= ntr2
   error([' The two input data sets must have the same number of traces (', ...
          num2str(ntr1),' differs from ',num2str(ntr2),')'])
end


if n2 >= n1-nf  % Number of samples of target >= number of samples of original
                % data set - filter coefficients
   [filt,cc_max,shifts]=mfilter_t2t(from.traces(ia1:ie1,:), ...
                        to.traces(ia2:ie2,:),nf,param.wnoise,incr);
   min_shift=min(shifts);
   dshift=(shifts-min_shift);
   max_shift=max(dshift);
   if max_shift > 0  &  isnan(param.null)
      filter.null=NaN; 
      filter.traces=NaN*ones(nf+max_shift,ntr1);
   else
      filter.traces=param.null*ones(nf+max_shift,ntr1);
   end

   for ii=1:ntr1
      filter.traces(1+dshift(ii):dshift(ii)+nf,ii)=filt(:,ii);
   end
   
else
   warning(' ... not yet sufficiently tested for multi-trace filters')
   nshifts=n1-nf-n2;
   ia10=ia1-1;
   ie10=ia10+n2+nf-2;
   cc=zeros(nshifts,ntr1);
   filters=zeros(nshifts,ntr1,nf);
   for ii=1:nshifts
      [filt,cc_max,shifts]=mfilter_t2t(from.traces((ia10:ie10)+ii,:), ...
                        to.traces(ia2:ie2,:),nf,param.wnoise,incr);
      filters(ii,:,:)=filt';
      cc(ii,:)=cc_max;
   end
   [cc_max,idx]=max(cc);
   idx1=idx+(0:nshifts:(ntr1-1)*nshifts);
   shifts=2-idx;
   filters=reshape(filters,[],nf);
   filters=filters(idx1,:)';

   min_shift=min(shifts);
   dshift=(shifts-min_shift);
   max_shift=max(dshift);
   if max_shift > 0  &  isnan(param.null)
      filter.null=NaN; 
      filter.traces=NaN*ones(nf+max_shift,ntr1);
   else
      filter.traces=param.null*ones(nf+max_shift,ntr1);
   end

   for ii=1:ntr1
      filter.traces(1+dshift(ii):dshift(ii)+nf,ii)=filters(:,ii);
   end
 
end

filter.step=from.step;
%filter.first=to.first-from.first-(nf-nf2-min_shift)*filter.step;
%filter.first=to.first-from.first-(nf-min_shift)*filter.step;
filter.first=ta2-ta1-(nf-min_shift)*filter.step;
filter.last=filter.first+(nf+max_shift-1)*filter.step;
if isfield(from,'headers')
  filter.headers=from.headers;
  filter.header_info=from.header_info;
end
% -----------------------------------------------

               case 'dataset'
if ntr1 ~= ntr2
  error([' The two input data sets must have the same number of traces (', ...
          num2str(ntr1),' differs from ',num2str(ntr2),')'])
end
if n2 >= n1-nf
   [filt,cc_max,shifts]=mfilter_d2d(from.traces(ia1:ie1,:), ...
                        to.traces(ia2:ie2,:),nf,param.wnoise,incr);
   filter.traces=filt;

else
   error(' Second data set too short (< length of "from" + filter length); not yet implemented')
end

filter.step=from.step;
filter.first=ta2-ta1-(nf-shifts)*filter.step;
filter.last=filter.first+(nf-1)*filter.step;
% -----------------------------------------------

               case 'all'
ntr12=ntr1*ntr2;
filters=zeros(nf,ntr12);
cc_max=zeros(ntr12,1);
shifts=zeros(ntr12,1);

if n2 >= n1-nf
   for ii=1:ntr1
      [filt,cc_m,shift,scale]=mfilter_t2d(from.traces(ia1:ie1,ii), ...
                        to.traces(ia2:ie2,:),nf,param.wnoise,incr,0);
      if scaling
%        filters(:,(ii-1)*ntr2+1:ii*ntr2)=filt*spdiags(scale,0, ...
%            length(scale),length(scale));
         filters(:,(ii-1)*ntr2+1:ii*ntr2)=mvt(filt,scale);
      else
         filters(:,(ii-1)*ntr2+1:ii*ntr2)=filt;
      end
      cc_max((ii-1)*ntr2+1:ii*ntr2)=cc_m;
      shifts((ii-1)*ntr2+1:ii*ntr2)=shift;
   end
   min_shift=min(shifts);
   dshift=(shifts-min_shift);
   max_shift=max(dshift);
   if max_shift > 0 & isnan(param.null)
      filter.null=NaN; 
      filter.traces=NaN*ones(nf+max_shift,ntr1*ntr2);
   else
      filter.traces=param.null*ones(nf+max_shift,ntr1*ntr2);
   end
   for ii=1:ntr12
      filter.traces(1+dshift(ii):dshift(ii)+nf,ii)=filters(:,ii);
   end
else
   error(' Not yet implemented')
end

filter.step=from.step;
% filter.first=to.first-from.first-(nf-nf2-min_shift)*filter.step;
filter.first=to.first-from.first-(nf-min_shift)*filter.step;
filter.last=filter.first+(nf+max_shift-1)*filter.step;
cc_max=reshape(cc_max,ntr2,ntr1);
% -----------------------------------------------

               otherwise
error([' Unknown option ',option])

               end

filter.units=from.units;

if ~isempty(param.header) & ~strcmpi(param.option,'dataset')
 filter=s_header(filter,'add',param.header,cc_max(:)','n/a', ...
              'Coefficient of correlation');
end

%    Append history field
if isfield(from,'history') & isfield(to,'history')
  filter.history=from.history;
  htext=param.option;
  filter=s_history(filter,'append',htext);
  filter=s_history(filter,'merge',to.history);
end